Best AI tools for< Chemistry Analyst >

Jotlify is an AI-powered platform that simplifies complex research papers, making them accessible and easy to understand for students, researchers, professionals, and curious minds. It transforms dense academic content into engaging stories and insights, bridging the gap between complex research and easy understanding. With Jotlify, users can uncover stories and insights that can transform their understanding and impact various aspects of their lives.

Julius AI is an advanced AI data analyst tool that allows users to analyze data with computational AI, chat with files to get expert-level insights, create sleek data visualizations, perform modeling and predictive forecasting, solve math, physics, and chemistry problems, generate polished analyses and summaries, save time by automating data work, and unlock statistical modeling without complexity. It offers features like generating visualizations, asking data questions, effortless cleaning, instant data export, creating animations, and supercharging data analysis. Julius AI is loved by over 1,200,000 users worldwide and is designed to help knowledge workers make the most out of their data.

EasySBC is a web-based application that provides solutions for Squad Building Challenges (SBCs) in the popular video game FIFA 23. It features an AI-powered squad builder that helps users create optimal squads for SBCs, taking into account player ratings, chemistry, and other factors. The application also includes a comprehensive database of players and their attributes, as well as meta ratings that indicate the effectiveness of players in different positions and formations.

Kuano is an AI tool that focuses on redefining drug discovery using Quantum and AI technologies. The platform offers world-class scientific expertise in quantum physics, AI, and medicinal chemistry to revolutionize the drug design process. Kuano aims to leverage cutting-edge technologies to accelerate the discovery of new drugs and improve healthcare outcomes.

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

Lavo Life Sciences is an AI-accelerated crystal structure prediction application that helps in drug development by providing accurate predictions for small molecule drugs. The application utilizes AI technology to optimize solid-state formulations, reduce turnaround time, mitigate risks, and discover novel polymorphs, ultimately streamlining the pharmaceutical research and development process.

C&EN, a publication of the American Chemical Society, provides the latest news and insights on the chemical industry, including research, technology, business, and policy. It covers a wide range of topics, including analytical chemistry, biological chemistry, business, careers, education, energy, environment, food, materials, people, pharmaceuticals, physical chemistry, policy, research integrity, safety, and synthesis.

One Drop has developed a next-generation intradermal continuous glucose monitoring (CGM) device. Advanced material science, chemistry, and electronics make the One Health CGM among the most innovative body-worn sensors—explicitly designed to meet the needs of people with type 2 diabetes. By integrating proprietary micro-needle technology and AI-enabled precision guidance, the minimally invasive One Health CGM will deliver pain-free, needle-free wear and unprecedented access to a population currently underserved by CGM.

Allchemy is a resource-aware AI platform for drug discovery. It combines state-of-the-art computational synthesis with AI algorithms to predict molecular properties. Within minutes, Allchemy creates thousands of synthesizable lead candidates meeting user-defined profiles of drug-likeness, affinity towards specific proteins, toxicity, and a range of other physical-chemical measures. Allchemy encompasses the entire resource-to-drug design process and has been used in academic, corporate and classified environments worldwide to: Design synthesizable leads targeting specific proteins Evolve scaffolds similar to desired drugs Design “circular” drug syntheses from renewable materials Interface with and instruct automated synthesis platforms and optimize pilot-scale processes Operate “iterative synthesis” schemes Predict side reactions and create forensic “synthetic signatures” of hazardous/toxic molecules Design synthetic degradation and recovery cycles for various types of feedstocks and functional target molecules

Chemprop is a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. Originally developed for research purposes, Chemprop offers a comprehensive set of tools and features for training models and analyzing molecular representations. The package underwent a recent major release (v2.0.0) with significant improvements and updates.

XtalPi is a world-leading technology company driven by artificial intelligence (AI) and robotics to innovate in the fields of life sciences and new materials. Founded in 2015 at the Massachusetts Institute of Technology (MIT), the company is committed to realizing digital and intelligent innovation in the fields of life sciences and new materials. Based on cutting-edge technologies and capabilities such as quantum physics, artificial intelligence, cloud computing, and large-scale experimental robot clusters, the company provides innovative technologies, services, and products for global industries such as biomedicine, chemicals, new energy, and new materials.

Valo is a company that uses AI-driven technology to transform the discovery and development of life-changing medicines. They combine machine learning, tissue biology, and patient data to create a suite of powerful capabilities that bring the future of drug discovery and development to bear. Valo's team of software engineers, data scientists, biologists, medicinal chemists, and big-picture thinkers are dedicated to advancing the combined power of technology and patient data.

Knowee is an AI-powered web application that provides 24/7 homework help, AI tutoring, and course notes. It offers high accuracy answers surpassing GPT-4 for various academic tasks. Users can upload images or drag and drop questions for assistance, get step-by-step solutions, and master diagrams. Knowee helps students review tough concepts, explore relevant questions, and enhance their grades across all subjects.

Gauth is an AI-powered homework helper that provides step-by-step solutions to STEM problems. It utilizes advanced algorithms and AI technology to solve complex math, statistics, calculus, physics, chemistry, biology, and history questions. Gauth also offers live expert support, with thousands of real experts available 24/7 to provide detailed explanations and guidance. The app is designed to help students of all grades and levels conquer challenging homework problems and improve their understanding of STEM subjects.

Quiz Wizard is an AI-powered tool that helps teachers and educators create quizzes, flashcards, and theoretical sheets on any topic in seconds. With Quiz Wizard, you can save time and provide tailored educational content for your students. Whether you're teaching physics, chemistry, languages, medicine, or any other subject, Quiz Wizard has got you covered.

Studdy AI is an AI tutoring application designed to help students learn and understand various subjects with ease. It offers step-by-step explanations for solving problems, allows users to ask questions when confused, and provides detailed breakdowns of concepts. The app has received positive feedback for its effectiveness in aiding students in subjects like math, chemistry, and ethics. Studdy AI stands out for its interactive learning approach that encourages active engagement and comprehension.

HomeworkAI is an AI-powered homework helper that provides step-by-step solutions to students and educational professionals of all levels. With HomeworkAI, users can upload their homework assignments, practice questions, or type in their questions to receive instant and accurate answers. HomeworkAI covers a wide range of subjects, including mathematics, physics, biology, chemistry, literature, and history. The platform is easy to use and provides clear and concise solutions, making it a valuable tool for students looking to improve their grades and understanding of the subject matter.

The website offers online tutoring services with the help of artificial intelligence in various languages such as German, English, Croatian, Polish, Turkish, Ukrainian, and Arabic. It provides personalized tutoring sessions, homework assistance, and explanations for a wide range of school subjects. The AI tool supports self-directed learning by adapting to the user's school level and learning progress. Users can receive help in over 30 school subjects, including math, geography, history, biology, chemistry, and more. The platform allows users to upload homework assignments, receive detailed explanations, and interact with AI tutors through chat sessions.

Science in the News is a Harvard graduate student organization with a mission to bridge the communication gap between scientists and non-scientists. It provides a platform for researchers to share their work with the wider community in an accessible and engaging way. The website features articles, podcasts, videos, and other resources on a wide range of scientific topics, including astronomy, biology, chemistry, computer science, and physics.

Alignerr is a platform powered by Labelbox that offers subject matter experts the opportunity to align AI models by creating high-quality data in their field of expertise. The platform aims to build the future of Generative AI by enabling experts to contribute to tasks such as coding improvement, data science synthesis, basic math and chemistry comprehension, and creative writing. Alignerr provides a transparent pay structure and allows individuals to work from home on their own schedule, earning up to $150/hr. Contributors can play a pivotal role in shaping the future of artificial intelligence by working on tasks that involve rating or ranking assignments, open rewrite tasks, and multi-modal assignments. The platform emphasizes the responsible development of AI technologies and offers flexibility for professionals to balance work with personal life effortlessly.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It enables benchmarking across a wide range of API-based models and employs an LLM-based extractor as a fallback mechanism. Users can evaluate models on specific chemistry topics and run comprehensive evaluations across all topics in the benchmark suite. The tool facilitates seamless benchmarking for any model supported by LiteLLM and allows running non-API hosted models.

2025-AI-College-Jobs is a repository containing a comprehensive list of AI/ML & Data Science jobs suitable for college students seeking internships or new graduate positions. The repository is regularly updated with positions posted within the last 120 days, featuring opportunities from various companies in the USA and internationally. The list includes positions in areas such as research scientist internships, quantitative research analyst roles, and other data science-related positions. The repository aims to provide a valuable resource for students looking to kickstart their careers in the field of artificial intelligence and machine learning.

Docling simplifies document processing, parsing diverse formats including advanced PDF understanding, and providing seamless integrations with the general AI ecosystem. It offers features such as parsing multiple document formats, advanced PDF understanding, unified DoclingDocument representation format, various export formats, local execution capabilities, plug-and-play integrations with agentic AI tools, extensive OCR support, and a simple CLI. Coming soon features include metadata extraction, visual language models, chart understanding, and complex chemistry understanding. Docling is installed via pip and works on macOS, Linux, and Windows environments. It provides detailed documentation, examples, integrations with popular frameworks, and support through the discussion section. The codebase is under the MIT license and has been developed by IBM.

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

Academic_LLM_Sec_Papers is a curated collection of academic papers related to LLM Security Application. The repository includes papers sorted by conference name and published year, covering topics such as large language models for blockchain security, software engineering, machine learning, and more. Developers and researchers are welcome to contribute additional published papers to the list. The repository also provides information on listed conferences and journals related to security, networking, software engineering, and cryptography. The papers cover a wide range of topics including privacy risks, ethical concerns, vulnerabilities, threat modeling, code analysis, fuzzing, and more.

ML Contests is a platform that provides a sortable list of public machine learning/data science/AI contests, viewable on mlcontests.com. Users can submit pull requests for any changes or additions to the competitions list by editing the competitions.json file on the GitHub repository. The platform requires mandatory fields such as competition name, URL, type of ML, deadline for submissions, prize information, platform running the competition, and sponsorship details. Optional fields include conference affiliation, conference year, competition launch date, registration deadline, additional URLs, and tags relevant to the challenge type. The platform is transitioning towards assigning multiple tags to competitions for better categorization and searchability.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

OlympicArena is a comprehensive benchmark designed to evaluate advanced AI capabilities across various disciplines. It aims to push AI towards superintelligence by tackling complex challenges in science and beyond. The repository provides detailed data for different disciplines, allows users to run inference and evaluation locally, and offers a submission platform for testing models on the test set. Additionally, it includes an annotation interface and encourages users to cite their paper if they find the code or dataset helpful.

A toolkit for computational chemistry research, featuring tools to facilitate automated workflows. Includes tools for NMR prediction, dynamic catalysis research, proton transfer analysis, amorphous oxides structure analysis, reweighting, and more. Users can install 'ai2-kit' via pip and explore various domain-specific and general tools for processing system data and filtering structures by model deviation.

This repository serves as an educational resource for pharmaceutical and chemistry students to learn the basics of Deep Learning through a collection of Jupyter Notebooks. The content covers various topics such as Introduction to Jupyter, Python, Cheminformatics & RDKit, Linear Regression, Data Science, Linear Algebra, Neural Networks, PyTorch, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks, Autoencoders, Graph Neural Networks, and Summary. The notebooks aim to provide theoretical concepts to understand neural networks through code completion, but instructors are encouraged to supplement with their own lectures. The work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

A guide to electron configurations with a fun, cartoony approach.

Send me an image of Math. I will give you the LaTeX code.

Expert in generating and explaining mathematical, chemical, and computational formulas.

Provides concise GC troubleshooting and method development advice that is easy to implement.

Expert in quantum mechanics and light spectral analysis.

深入分析无机材料及氧离子导体论文指南，主要使用中文回答。

Analyzes IR spectra, prompts for uploads, and details findings in tables.

Provides concise HPLC troubleshooting and method development advice

I give fun, imaginative taste descriptions, sans health warnings.

An educational guide to exploring the world of chemistry.

Answer Chemistry related Questions and exercises

Turbocharge your research and streamline your path to breakthrough findings. Leveraging the vast resources of PubChem, this GPT taps into a wealth of chemical data—from substances to proteins and patents—unleashing the full potential of your data for richer, more informed discoveries.

Advanced AI for chemistry, offering innovative solutions, process optimizations, and safety assessments, powered by OpenAI.

Professional chemistry assistant, SMILES/SMART supported molecule and reaction diagrams, and more!

Opening lines for dating apps

Fun and detailed chemistry insights in cooking, with equipment tips.

AQA Chemistry Study Assistant

Explore the fascinating world of elements with in-depth insights on chemistry, periodic table, and elemental properties. Dive into our comprehensive guide for a deeper understanding of the elements' significance in science and everyday life.

Find and summarize recent papers in biology, chemistry, and biomedical sciences.

Your versatile guide for all things craft beer, from recipes & chemistry to production & packaging. v0.5.4.